Beneficiary Experience of Care by Level of Integration in Dual Eligible Special Needs Plans

This cross-sectional study compares experience of care, out-of-pocket spending, and satisfaction between dually enrolled Medicaid beneficiaries with traditional Medicare and those with Dual Eligible Special Needs Plans with and without exclusively aligned enrollment.

We tested for used to for differences in means for each trait or in the percent of respondents with each trait among survey respondents in each strata relative to all surveyed individuals (total sample), and use asterisks to indicate difference that were statistically significant as follows: *p < 0:10 **p < 0:05 ***p < 0:01.b) EAE: Exclusively aligned enrollment.c) Self-reported by respondents in Medicaid enrollment records.d) Residence in a rural area based on RUCA codes developed by the USDA.

eAppendix 2. Additional Details on Methods
Below we provide additional details on the methods we used in the estimation of regression results reported in Tables 2, 3, and 4 of the main paper.Specifically, we combined multivariate regression analysis of outcomes with the use of propensity score weighting to compare dually eligible beneficiaries with different types of Medicare plans.Our work first compares three groups of members (two D-SNP treatments to traditional Medicare), and second compares two groups of members (one combined D-SNP treatment to traditional Medicare).We used a multi-step estimation described in (a) and (b) below to generate results reported in the manuscript and we checked that we obtained similar results using simultaneous estimation with generalized estimating equations, as described in (c) below.

(a) Comparing two different types of D-SNPs to traditional Medicare
A key study objective was to examine differences in outcomes by enrollment in two types of D-SNP (namely, D-SNPs with exclusively aligned enrollment and D-SNPs without exclusively aligned enrollment).
To compare outcomes for members in either D-SNP coverage group to members in traditional Medicare and to those in the other D-SNP coverage group, we estimated the following regression equation: (1) Y i = β 0 + β 1 X i + γ D-SNP-EAE i + δ D-SNP-OTHER I + ε i where Y i represents one of the outcomes we examine (pertaining to either patient experience of care, out-of-pocket spending, or patient satisfaction) for individual i, X i is a set of respondent-level traits, and ε i is a normally distributed error term.X i includes variables measuring demographic traits, including six indicators for age (less than 45 years, 45-64 years, 70-74 years, 75-79 years, 80-84 years, and age 85 and older, relative to those age 65-70), an indicator for sex (female relative to male), two indicators for Black race and other race (relative to White race), Hispanic ethnicity (relative to non-Hispanic), two indicators for highest level of education (less than a high school education and high school education, relative to more than a high school education), three indicators for marital status (never married, separated/divorced, or widowed, relative to married), and an indicator for residence in a rural area (relative to non-rural).X i also includes a count of limitations in up to six activities of daily living (ADLs) (including difficulties bathing/showering, dressing, eating, getting in and out of bed or chairs, walking, and toileting) and indicators for whether a physician or other health provider ever told the person that they had a heart condition, stroke, chronic obstructive pulmonary disease, cancer, diabetes, asthma, an intellectual or developmental disability, depression/anxiety or another mental health problem, a substance use disorder, dementia, or any other chronic disease.The key explanatory variables in Equation (1) are two indicators for members enrolled in D-SNPs with exclusively aligned enrollment (D-SNP-EAE i ) and members enrolled in D-SNPs without exclusively aligned enrollment (D-SNP-OTHER I ), relative to members in traditional Medicare.
In estimating Equation (1) we used propensity score weighting to account for non-random selection into types of Medicare coverage.2] Given that our first set of analysis compares two treatment groups (D-SNP-EAE i and D-SNP-OTHER I ) relative to the control group (traditional Medicare), we estimated propensity scores from a multinomial logistic regression model where the dependent variable indicated one of three categories of Medicare coverage. 3As explanatory variables in the model we included the set of controls shown in X i in Equation (1) and measuring demographic traits, clinical indicators, and the count of ADL limitations.
We used the estimated model coefficients from the multinomial logistic regression to construct inverse probability of treatment weights (IPTW), as follows: (2) w i = 1 Pr(  = |   ) where M i is the Medicare coverage type for individual i and j takes values 1, 2, or 3 for traditional Medicare coverage, D-SNP coverage without EAE, and D-SNP coverage with EAE, respectively.Applying these weights in the estimation of Equation (1) thus places a higher weight on persons with lower probabilities of being in their observed category of Medicare coverage.We estimated robust standard errors of the coefficient estimates in the outcome (Equation 1) models.We used the estimated coefficients from the IPTW weighted regression models of Equation ( 1) to report estimated differences in care by type of Medicare coverage in Tables 2, 3, and 4 of the paper, including the adjusted differences in outcomes between persons enrolled in D-SNPs with exclusively aligned enrollment relative to those with traditional Medicare (given by  ̂) and adjusted differences between persons enrolled in D-SNPs without exclusively aligned enrollment relative to those with traditional Medicare (given by  ̂), and adjusted differences between persons in D-SNPs with exclusively aligned enrollment and those in other types of D-SNPs (given by  ̂ - ̂).For all estimates we report 95% confidence intervals and p-values.

(b) Comparing members in both types D-SNPs combined to traditional Medicare
We also examined differences in outcomes by enrollment in either type of D-SNP relative to members in traditional Medicare.We estimated the following regression equation: As in Equation (1) Y i represents one of the outcomes we examine (pertaining to either patient experience of care, out-of-pocket spending, or patient satisfaction), X i is the same set of respondent-level traits, and ε i is a normally distributed error term.The key explanatory variable is ANY D-SNP, which equals one if the respondent is enrolled in either a D-SNP with EAE or another type of D-SNP and zero if the respondent is enrolled in traditional Medicare.As with the estimation of Equation (1), we also applied weights defined from a propensity score model.In this case, we estimated propensity scores from a logit regression of the single binary treatment (ANY-D-SNP) on Xi, and we calculated the propensity score (PS) as the probability of being in either type of D-SNP conditional on the person's characteristics (X).
We calculated inverse probability of treatment weights for each individual in either D-SNP plan as equal to 1/PS, and for each individual with traditional Medicare as equal to 1/(1−PS).This assigns larger weights to individuals in D-SNPs with a lower probability of being in a D-SNP as well as to individuals in traditional Medicare with a higher probability of being in a D-SNP.We estimated robust standard errors of the coefficient estimates in the outcome (Equation 3) models.Adjusted differences in outcomes between persons enrolled in either type of D-SNPs relative to those with traditional Medicare (given by  ̂) are reported in the far-right column of Tables 2, 3, and 4. eFigure 2. Distribution of propensity scores by treatment group (single treatment group)

(c) Use of GMM Estimation
In addition to the multi-step approach described above, we also estimated all results from Tables 2, 3, and 4 using generalized method of moments (GMM) estimation that simultaneously estimate the propensity scores and outcome model, using the teffects procedure in Stata with IPT weighting and regression adjustment for both single and multivalued treatments.We obtained very similar results in support of our study conclusions (available on request).

Medical equipment
Feel OOP costs are a major financial burden, % How much of a financial burden were the out-of-pocket costs you paid for your health care?"Major burden" versus "Minor burden" and "Not a burden at all" % Very satisfied with: Care Coordinator Thinking about all the care coordination you may have received from you Medicaid health plan coordinator in the last 6 months…Overall are you currently satisfied or dissatisfied with the care coordination services you receive?

PCP Choice
The choice of primary care physicians offered by your health plan?"Very Satisfied" versus "Somewhat Satisfied," "Somewhat Unsatisfied," and "Very Unsatisfied" Specialist choice The choice of specialist physicians offered by your health plan?
" " Customer Service: Always gives needed info/help How often did your health plan's customer service give you the information or help you needed?
"Always" versus "Usually," "Sometimes," and "Never" Always treats with courtesy/respect How often did your health plan's customer service treat you with courtesy and respect?
" " Plan rating, on scale of 1-10 Using any number from 0-10, where 0 is the worst health plan possible and 10 is the best health plan possible, what number would you use to rate your Medicaid health plan?"0-Worst health plan possible" through "10-Best health plan possible" % rated plan a 10 "10-Best health plan possible" % Strongly Agree: Do you agree with the following statements?Know who to call about health/health care I know who to call when I have questions about my health or healthcare "Strongly Agree" versus "Agree," "Disagree" and "Strongly Disagree" Confident in understanding of health care system I feel confident in my understanding of the healthcare system " "

Caring for health/chronic conditions is manageable
Caring for my health and chronic conditions is manageable " " SOURCE: Author's analyses of 2022 Commonwealth Coordinated Care Plus (CCCP) member survey data.NOTES: a) HCBS (Home and Community Based Services), defined in the survey as "care and other in-home services and conveniences that help with daily activities (personal care services, adult day care, skilled nursing, etc.); b) OOP: Out-of-pocket.

eTable 3 .
Dually Eligible Beneficiaries' Access to Care, Overall, and by Type of Medicare Plan; Using Alternate Definitions eTable 4. Dually Eligible Beneficiaries' Experience of Care, Overall, and by Type of Medicare Plan; Using Alternate Sample eAppendix 5. Survey Questions and Response Coding Used in Outcome Measure Construction Full model results of Dually Eligible Beneficiaries' Experience of Care (for models reported in Table 2, D-SNP without EAE and D-SNP with EAE, relative to Traditional Medicare) eTable 7. Full Model Results of Dually Eligible Beneficiaries' Out-of-Pocket Spending (for models reported in Table 3, D-SNP without and D-SNP with EAE, relative to Traditional Medicare)

eAppendix 3. Covariate Balance After Weighting When
observations were weighted by propensity score weights, we observed no statistically significant differences in member demographic traits, number of ADL limitations, and clinical diagnoses, as shown in eTable 2. eTable 2. Differences in Dually Eligible Beneficiaries' Demographic Traits, ADLs, and Diagnosed Conditions, by Medicare Plan Type a, Propensity Score Weighted Proportions and Means -values report results from tests of significant differences in the proportion of respondents with each trait (or variable mean in the case of the ADL limitation count) among dually eligible beneficiaries in each type of D-SNP relative to dually eligible beneficiaries enrolled in traditional Medicare.Estimates are based on 1,913 observations with complete data on the respondent characteristics in the far-left column.b) EAE: Exclusively aligned enrollment.c) Self-reported by respondents.Other race respondents included persons who identified as Asian, Native American, more than one race, or some other race not indicated.d) Residence in a rural area based on RUCA codes developed by the USDA.e) Number of activities of daily living that a respondent reported difficulty performing alone and without using special equipment.

eAppendix 4. Sensitivity Analysis eTable 3. Dually
Eligible Beneficiaries' Access to Care, Overall, and by Type of Medicare Plan; Using Alternate Definitions a *

eAppendix 5. Survey Questions and Response Coding Used in Outcome Measure Construction For
each outcome (dependent variable) used in the analysis reported in Tables 2-4, we report the text of the survey question and the details on the variable coding in eTable 3 below.

eAppendix 6. Full Regression Model Results eTable 6:
Full model results of Dually Eligible Beneficiaries' Experience of Care (for models reported in Table2, D-SNP without EAE and D-SNP with EAE, relative to Traditional Medicare) Difficulty with a : Difficulty is defined as having a lot of difficulty or some difficulty, versus no difficulty/no need.b)HCBS(Home and Community Based Services), defined in the survey as "care and other in-home services and conveniences that help with daily activities (personal care services, adult day care, skilled nursing, etc.) Difficulty is defined as having a lot of difficulty or some difficulty, versus no difficulty/no need.b)HCBS(Home and Community Based Services), defined in the survey as "care and other in-home services and conveniences that help with daily activities (personal care services, adult day care, skilled nursing, etc.) Full Model Results of Dually Eligible Beneficiaries' Out-of-Pocket Spending (for models reported in Table3, D-SNP without and D-SNP with EAE, relative to Traditional Medicare) Full Model Results of Dually Eligible Beneficiaries' Satisfaction (for models reported in Table 4, D-SNP without EAE and D-SNP with EAE, relative to Traditional Medicare) Very satisfied with: Full Model Results for Dually Eligible Beneficiaries' Experience of Care (models reported in

Table 2 ,
Any D-SNP, relative to Traditional Medicare) Difficulty with a : Difficulty is defined as having a lot of difficulty or some difficulty, versus no difficulty/no need.b)HCBS(Home and Community Based Services), defined in the survey as "care and other in-home services and conveniences that help with daily activities (personal care services, adult day care, skilled nursing, etc.) Full Model Results of Dually Eligible Beneficiaries' Out-of-Pocket Spending (for models reported Table3, Any D-SNP, relative to Traditional Medicare) Full Model Results of Dually Eligible Beneficiaries' Satisfaction (from models reported Table 4, Any D-SNP, relative to Traditional Medicare) Very satisfied with: © 2024 Mellor JM et al.JAMA Health Forum.